Bayesian integration of flux tower data into process-based simulator for quantifying uncertainty in simulated output

  • Nick Hamm (Contributor)
  • Alfred Stein (Contributor)
  • Rahul Raj (Creator)

Dataset

Description

This research implemented a Bayesian statistical method to calibrate a widely used process-based simulator BIOME-BGC against estimates of gross primary production (GPP) data. Six parameters of BIOME-BGC were calibrated, which were also allowed to vary month-by-month to investigate the hypothesis that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. Time varying parameters substantially improved the simulated GPP as compared to GPP obtained with constant parameters.

Process-based simulator, BIOME-BGC, Gross primary production, Bayesian calibration, uncertainty estimation
Date made available14 Dec 2016
PublisherDANS easy
Temporal coverageApr 2009 - Oct 2009
Date of data production1 Sep 2016
Geographical coverageSpeulderbos forest site, The Netherlands
Geospatial point52.257441, 5.686968

Cite this

Hamm, N. (Contributor), Stein, A. (Contributor), Raj, R. (Creator) (14 Dec 2016). Bayesian integration of flux tower data into process-based simulator for quantifying uncertainty in simulated output. DANS easy. 10.17026/dans-zc7-7549